{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vizualizing meandering of 2D stream\n",
    "\n",
    "Inspired from work of Zoltán Sylvester,Bureau of Economic Geology, and made with Dr. Al Ibrahim for Quantitative Sequence Stratigraphy project.\n",
    "\n",
    "Added features:\n",
    "- Stream cutoff when segments meet, forming an ox-bow lake.\n",
    "- Removed achoring artifact.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "import scipy.interpolate\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (9,5) # set the default figure size to (9,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sine curve\n",
    "\n",
    "Let's start with a simple sine curve. This is what Petrel uses as its basic channel object generation method. However, most meandering rivers do not follow a sine curve pattern, unless they are of low sinuosity. You can use the sliders in the interactive widget to change the phase, amplitude, and wavelength of the centerline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "910bd5943f9c43f99d6db01fa9f2a4b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=105, description='theta0d', max=180), FloatSlider(value=1.0, description…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(theta0d=(0,180,1),amp=(0.1,10,0.1),lamb=(1,10,0.1))\n",
    "def sine(theta0d=105.0,amp=1,lamb=1):\n",
    "    theta0 = theta0d*pi/180 # convert degrees to radians\n",
    "    x = np.arange(0, 18, 0.01)\n",
    "    y = amp*np.sin(theta0+lamb*x)\n",
    "    plt.plot([0,18],[0,0],'k--')\n",
    "    plt.plot(x,y,lw=5,color=(0.04,0.37,0.59))\n",
    "    plt.axis('equal')\n",
    "    plt.xlim(0,2*9)\n",
    "    plt.ylim(-2*3,2*3)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sine-generated curve\n",
    "\n",
    "The sine-generated curve matches much better the observed shapes of well-developed natural meanders, largely because curvature changes more smoothly than it does in the case of a sine curve. It was introduced by Langbein and Leopold (Langbein, W., and Leopold, L., 1966, River meanders—theory of minimum variance: US Geol. Survey Prof. Paper 422) and it is based on the idea that it is not the channel centerline itself, but the direction of the centerline that is a sinusoidal function:\n",
    "\n",
    "$$\\theta = \\omega sin\\Big(\\frac{2\\pi}{\\lambda} s \\Big),$$\n",
    "\n",
    "where $\\theta$ is the channel direction, $\\omega$ is the maximum angle between the channel and the downvalley axis, $\\lambda$ is a scale parameter, and $s$ is the along-channel distance. The drawbacks of the sine-generated curve include the lack of asymmetry and the inability to generate longer centerlines that have meander bends of different shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2b22f481fd048198d6a6fadfa394a9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=2500, description='lamb', max=5000, min=100, step=100), FloatSlider(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(lamb=(100,5000,100), theta0d=(0.0,140.0,1.0), slen=(1000,100000,100))\n",
    "def sine_generated(lamb=2500,theta0d=105.0,slen=20000):\n",
    "    ds = 25\n",
    "    s = np.arange(0, slen, ds)\n",
    "    theta0 = theta0d*pi/180 # convert to radians\n",
    "    theta = theta0*np.sin(2*pi*s/lamb)\n",
    "    x = np.cumsum(ds*np.cos(theta))\n",
    "    y = np.cumsum(ds*np.sin(theta))\n",
    "    x = np.hstack((0,x)) # add 0 at the beginning\n",
    "    y = np.hstack((0,y))\n",
    "    plt.plot(x,y, lw=5, color=(0.04,0.37,0.59))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.xlim(0,3000)\n",
    "    plt.ylim(-600,1400)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolution (Howard & Knutson) model\n",
    "\n",
    "The Howard and Knutson model is based on the calculation of an adjusted channel migration rate $R_1$ from a nominal migration rate $R_0$, using a weighting function $G(\\xi)$:\n",
    "\n",
    "$$ R_1(s) = \\Omega R_0(s) + \\Big(\\Gamma \\int_{0}^{\\infty}R_0(s-\\xi)G(\\xi)d\\xi\\Big) \\Big(\\int_{0}^{\\infty}G(\\xi)d\\xi\\Big)^{-1} $$\n",
    "\n",
    "$$ R_0 = k_l \\frac{W}{R} $$\n",
    "                                                  \n",
    "where $R_0(s)$ and $R_0(s-\\xi)$ are the nominal migration rates at locations $s$ and at a distance $\\xi$ upstream from $s$, respectively. \\Omega and \\Gamma are weighting parameters that are set to -1 and 2.5 respectively, to produce one of the two parameterizations of stable meandering (Howard and Knutson, 1984). $G(\\xi)$ is a weighting function that decreases exponentially in the upstream direction:\n",
    "\n",
    "$$ G(\\xi) = e^{-\\alpha\\xi} $$\n",
    "\n",
    "For additional details see the supplementary material to the following paper: Sylvester, Z., and Covault, J.A., 2016, Development of cutoff-related knickpoints during early evolution of submarine channels: Geology, v. 44, no. 10, p. 835–838, doi: 10.1130/G38397.1.\n",
    "\n",
    "Note that the script below does not address meander cutoffs; once the centerline starts intersecting itself, the results are meaningless. The way cutoffs are treated is an important (and non-trivial) part of a long-term evolution model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Remove Ox-bow lakes at intersections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# based on https://www.mathworks.com/matlabcentral/fileexchange/13351-fast-and-robust-self-intersections\n",
    "def selfIntersect(x,y):\n",
    "    # Create output\n",
    "    x0=[];\n",
    "    y0=[];\n",
    "    segments=[];\n",
    "\n",
    "    # Two similar curves are firstly created.\n",
    "    x1=x; x2=x;\n",
    "    y1=y; y2=y;\n",
    "\n",
    "    # Compute number of line segments in each curve and some differences we'll need later.\n",
    "    n1 = len(x1) - 1;\n",
    "    n2 = len(x2) - 1;\n",
    "\n",
    "    dxy1 = np.diff(np.array([x1, y1]).T, axis=0);\n",
    "    dxy2 = np.diff(np.array([x2, y2]).T, axis=0);\n",
    "\n",
    "\n",
    "    # Determine the combinations of i and j where the rectangle enclosing the\n",
    "    # i'th line segment of curve 1 overlaps with the rectangle enclosing the\n",
    "    # j'th line segment of curve 2.\n",
    "    r1 = np.tile(np.min(np.array([x1[0:-1],x1[1:]]).T, axis=1),[n2,1]).T\n",
    "    r2 = np.tile(np.max(np.array([x2[0:-1],x2[1:]]).T, axis=1),[n1,1])\n",
    "    r3 = np.tile(np.max(np.array([x1[0:-1],x1[1:]]).T, axis=1),[n2,1]).T\n",
    "    r4 = np.tile(np.min(np.array([x2[0:-1],x2[1:]]).T, axis=1),[n1,1])\n",
    "    r5 = np.tile(np.min(np.array([y1[0:-1],y1[1:]]).T, axis=1),[n2,1]).T\n",
    "    r6 = np.tile(np.max(np.array([y2[0:-1],y2[1:]]).T, axis=1),[n1,1])\n",
    "    r7 = np.tile(np.max(np.array([y1[0:-1],y1[1:]]).T, axis=1),[n2,1]).T\n",
    "    r8 = np.tile(np.min(np.array([y2[0:-1],y2[1:]]).T, axis=1),[n1,1])\n",
    "    [j, i] = np.where(np.logical_and(np.logical_and(r1<=r2, r3>=r4), np.logical_and(r5<=r6, r7>=r8)))\n",
    "\n",
    "    # Removing coincident and adjacent segments.\n",
    "\n",
    "    remove=np.where(np.abs(i-j)<2)\n",
    "    i = np.delete(i, remove)\n",
    "    j = np.delete(j, remove)\n",
    "\n",
    "    # Removing duplicate combinations of segments.\n",
    "    remove=np.array([], dtype='int');\n",
    "    for ii in range(0, len(i)):\n",
    "        ind = np.where(np.logical_and((i[ii]==j[ii:]), j[ii]==i[ii:]));\n",
    "        ind = np.array(ind);\n",
    "        remove= np.append(remove,ii+ind)   \n",
    "    i = np.delete(i, remove)\n",
    "    j = np.delete(j, remove)\n",
    "\n",
    "\n",
    "    # Find segments pairs which have at least one vertex = NaN and remove them.\n",
    "    # This line is a fast way of finding such segment pairs.  We take\n",
    "    # advantage of the fact that NaNs propagate through calculations, in\n",
    "    # particular subtraction (in the calculation of dxy1 and dxy2, which we\n",
    "    # need anyway) and addition.\n",
    "    remove = np.where(np.isnan(np.sum(dxy1[i,:] + dxy2[j,:],axis=1)));\n",
    "    i = np.delete(i, remove)\n",
    "    j = np.delete(j, remove)\n",
    "\n",
    "\n",
    "    xNew = np.copy(x)\n",
    "    yNew = np.copy(y)\n",
    "    xCut = np.copy(x)\n",
    "    yCut = np.copy(y)\n",
    "\n",
    "    remove=np.array([], dtype='int');\n",
    "    for ii in range(0,len(i)):\n",
    "        remove = np.append(remove, np.arange(j[ii]+1,i[ii]+1))\n",
    "\n",
    "    xNew = np.delete(xNew, remove)\n",
    "    yNew = np.delete(yNew, remove)\n",
    "    \n",
    "    keep = np.setdiff1d(np.array(np.arange(0,len(x))), remove)\n",
    "    xCut[keep] = np.nan\n",
    "    yCut[keep] = np.nan\n",
    "    \n",
    "    return xNew, yNew, xCut, yCut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x283646f15c8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data\n",
    "N=201;\n",
    "th=np.linspace(-3*pi,4*pi,N);\n",
    "R=1;\n",
    "x=R*np.cos(th)+np.linspace(0,6,N);\n",
    "y=R*np.sin(th)+np.linspace(0,1,N);\n",
    "xNew, yNew, xCut, yCut = selfIntersect(x,y)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x,y)\n",
    "plt.plot(xNew,yNew)\n",
    "plt.plot(xCut, yCut, 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = 5.0 # channel depth in meters\n",
    "W = D*20.0 # channel width in meters\n",
    "kl = 1.5E-7 # lateral erosion rate constant (m/s) \n",
    "dt = 6*30*24*60*60 * W/100 # time step in seconds (~ 6 months for W = 100 m)\n",
    "Cf = 0.03 # dimensionless Chezy friction factor\n",
    "nit = 1000 # number of time steps\n",
    "saved_ts = 1 # every 'saved_ts' time step is saved \n",
    "delta_s = W*0.25\n",
    "noisy_len = 8000 # length of noisy part of initial centerline\n",
    "pLeft = 20 # length of padding (no noise on this)\n",
    "pRight = 2\n",
    "k = 1 # constant in equation for exponent in curvature calculation \n",
    "omega = -1.0 # constant in curvature calculation (Howard and Knutson, 1984)\n",
    "gamma = 2.5 # from Ikeda et al., 1981 and Howard and Knutson, 1984\n",
    "alpha = k*2*Cf/D # exponent for convolution function G\n",
    "p=[]\n",
    "# CREATE INITIAL CENTERLINE COORDINATES\n",
    "x = np.linspace(0, noisy_len+(pLeft+pRight)*delta_s, int(noisy_len/delta_s+(pLeft+pRight))+1) \n",
    "y = 1.0 * (2*np.random.random_sample(int(noisy_len/delta_s)+1,)-1)\n",
    "ypadLeft = np.zeros((pLeft),)\n",
    "ypadRight = np.zeros((pRight),)\n",
    "y = np.hstack((ypadLeft,y,ypadRight))\n",
    "\n",
    "dx = np.diff(x); dy = np.diff(y)      \n",
    "ds = np.sqrt(dx**2+dy**2) # initial distances between points along centerline\n",
    "dxMax = dx[0];\n",
    "\n",
    "# LISTS FOR STORING RESULTS\n",
    "X, Y = [], []\n",
    "OXBOWX, OXBOWY = [], []\n",
    "CURVATURE = []\n",
    "MIGRATION_RATE=[]\n",
    "NOMINAL_MIGRATION_RATE=[]\n",
    "TIME = []\n",
    "\n",
    "\n",
    "yCut = []\n",
    "xCut = []\n",
    "\n",
    "for itn in range(1,nit): # main loop\n",
    "    ns=len(x)    \n",
    "    \n",
    "    # COMPUTE CURVATURE\n",
    "    ddx = np.diff(dx); ddy = np.diff(dy);      \n",
    "    curv = W*(dx[1:]*ddy-dy[1:]*ddx)/((dx[1:]**2+dy[1:]**2)**1.5) # curvature\n",
    "    # COMPUTE MIGRATION RATE\n",
    "    R0 = kl*curv # nominal migration rate\n",
    "    R1 = np.zeros(ns-2) # preallocate adjusted channel migration rate\n",
    "    for i in range(pLeft,ns-pRight):\n",
    "        si2 = np.cumsum(ds[i::-1]) # distance along centerline, backwards from current point\n",
    "        G = np.exp(-alpha*si2) # convolution vector\n",
    "        R1[i-1] = omega*R0[i-1] + gamma*np.sum(R0[i-1::-1]*G[:-1])/np.sum(G[:-1]) # adjusted migration rate\n",
    "    # COMPUTE NEW COORDINATES\n",
    "    x[pLeft:ns-pRight] = x[pLeft:ns-pRight] + R1[pLeft-1:ns-pRight-1]*dt*np.diff(y[pLeft:ns-pRight+1])/ds[pLeft:ns-pRight]\n",
    "    y[pLeft:ns-pRight] = y[pLeft:ns-pRight] - R1[pLeft-1:ns-pRight-1]*dt*np.diff(x[pLeft:ns-pRight+1])/ds[pLeft:ns-pRight]   \n",
    "    \n",
    "    y[ns-pRight-pRight:] = y[ns-pRight-pRight:]*0+y[ns-pRight-pRight-1] \n",
    "    x, y, xCut, yCut  = selfIntersect(x,y)\n",
    "    \n",
    "    # RESAMPLE CENTERLINE\n",
    "    tck, u = scipy.interpolate.splprep([x,y],s=0) # parametric spline representation of curve (for resampling)\n",
    "    unew = np.linspace(0,1,1+int(np.sum(ds)/delta_s)) # vector for resampling\n",
    "    out = scipy.interpolate.splev(unew,tck) # actual resampling\n",
    "    x = out[0]\n",
    "    y = out[1]\n",
    "    \n",
    "    # COMPUTE DISTANCES BETWEEN NEW POINTS\n",
    "    dx = np.diff(x); dy = np.diff(y)      \n",
    "    ds = np.sqrt(dx**2+dy**2) # distances between points along centerline\n",
    "    # STORE RESULTS\n",
    "    if np.mod(itn,saved_ts)==0:\n",
    "        X.append(x)\n",
    "        Y.append(y)\n",
    "        OXBOWX.append(xCut)\n",
    "        OXBOWY.append(yCut)\n",
    "        CURVATURE.append(curv)\n",
    "        TIME.append(itn)\n",
    "        MIGRATION_RATE.append(R1)\n",
    "        NOMINAL_MIGRATION_RATE.append(R0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b78d950685d74e1ea79c0d4d9529f437",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='ts', max=998, step=30), IntSlider(value=0, description='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backInTime = 400\n",
    "\n",
    "@interact(ts=(0,len(X)-1,30),xloc=(-1000,7000,50),zoom=(-5000,5000,100))\n",
    "def plot_clines(ts=0,xloc=0,zoom=-2000):\n",
    "    fig = plt.figure(num=None, figsize=(14, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    ax = plt.axes()\n",
    "    plt.text(0.01, 0.95, 'Timestep: ' + str(TIME[ts]), horizontalalignment='left', verticalalignment='top', transform=ax.transAxes, fontsize=28)\n",
    "    plt.text(.025, .57, 'Flow', horizontalalignment='left', verticalalignment='bottom', fontsize=20, transform=ax.transAxes)\n",
    "    ax.arrow(.005, .55, .1,0, width = .01, head_width=.05, head_length=.01, fc='k', ec='k', transform=ax.transAxes)\n",
    "\n",
    "    startTime = np.max(np.array([0,ts-backInTime]))\n",
    "    for i in np.arange(startTime,ts,20):\n",
    "        plt.plot(X[i],Y[i],lw=1,color=(0.5,0.5,0.5))\n",
    "    for i in np.arange(startTime,ts,1):\n",
    "        if np.any(~np.isnan(OXBOWX[i])):\n",
    "            plt.plot(OXBOWX[i],OXBOWY[i],lw=2,color=(1,.7,0))\n",
    "        \n",
    "    plt.plot(X[ts],Y[ts],lw=6,color=(0.04,0.37,0.59))\n",
    "    plt.plot(OXBOWX[ts],OXBOWY[ts],lw=6,color=(1,.7,0))\n",
    "    plt.xticks([]) \n",
    "    plt.yticks([])\n",
    "    plt.xlim(xloc,np.max(X[0][-1])+800)\n",
    "    plt.ylim(-(3000-zoom)/3.0,(3000-zoom)/3.0)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28363ed4a08>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "studiedPoint = 250\n",
    "\n",
    "yToPlot1=[]\n",
    "yToPlot2=[]\n",
    "yToPlot3=[]\n",
    "xToPlot1=[]\n",
    "xToPlot2=[]\n",
    "xToPlot3=[]\n",
    "for i in range(1, len(CURVATURE)):\n",
    "    yToPlot1.append(CURVATURE[i][studiedPoint])\n",
    "    yToPlot2.append(MIGRATION_RATE[i][studiedPoint+5])\n",
    "    yToPlot3.append(NOMINAL_MIGRATION_RATE[i][studiedPoint])\n",
    "    xToPlot1.append(i)\n",
    "    xToPlot2.append(i)\n",
    "\n",
    "fig = plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.plot(xToPlot1, yToPlot1)\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "plt.plot(xToPlot2, yToPlot2, 'r')\n",
    "#ax2 = ax.twinx()\n",
    "#plt.plot(xToPlot2, yToPlot3, 'g')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
